| --- |
| base_model: Qwen/Qwen3.5-9B |
| library_name: peft |
| license: apache-2.0 |
| pipeline_tag: image-text-to-text |
| tags: |
| - lora |
| - peft |
| - presentation |
| - slide-editing |
| - visual-qa |
| --- |
| |
| # EditPPT Vision Validator (LoRA on Qwen3.5-9B) |
|
|
| A LoRA adapter that checks whether an edit to a PowerPoint slide **newly introduced a severe visual defect**, |
| and if so, returns a repair as tool calls — in one generation. It serves as the vision validator of EditPPT. |
|
|
| Given the slide rendered **before** and **after** an edit, plus the edit instruction, the tool calls that made the |
| edit and the slide's shape JSON, the model returns: |
|
|
| ```json |
| {"HasCriticalIssues": "Yes", |
| "Issues": [{"IssueType": "ELEMENT_OVERLAP", "AffectedShapeIDs": [2], |
| "TechnicalDiagnosis": "textbox 2 overlaps the caption below it by about 20 pt"}], |
| "ToolCalls": [{"name": "adjust_layout", "arguments": {"slide_number": 3, "shape_id": 2, "top": 470}}]} |
| ``` |
|
|
| * **Defect types:** `CONTENT_OFF_SLIDE`, `ELEMENT_OVERLAP`, `LOW_CONTRAST`. |
| * **Repair tools:** `set_shape_font_size`, `adjust_layout`, `set_text_style`. Values are absolute targets in slide points. |
| * Defects already present in the BEFORE image are treated as the deck's own design and are not reported. |
|
|
| ## Usage |
|
|
| See `inference_example.py` and `prompt_template.txt`. |
|
|
| * Message order: `[image BEFORE, image AFTER, prompt text]`. |
| * Images: 960x540 (`min_pixels = max_pixels = 960*540`). One slide point = one pixel for a 16:9 deck. |
| * Greedy decoding, default chat template. |
|
|
| ## Training |
|
|
| | | | |
| |---|---| |
| | Base | Qwen3.5-9B, bf16 | |
| | Adapter | LoRA, r 16, alpha 32, on the language model and the vision encoder | |
| | Data | 7,260 training samples (plus 374 for validation) with human-confirmed labels, from publicly licensed decks (CC0 / CC-BY) | |
|
|
| ## Evaluation |
|
|
| Held-out set of 750 samples from decks not seen in training: |
|
|
| | Metric | Value | |
| |---|---| |
| | Verdict accuracy, edits without a repair (n=497) | 0.932 (base model: 0.60) | |
| | Verdict accuracy, all 750 samples | 0.955 | |
| | Defect precision / recall / F1, all 750 samples | 0.963 / 0.954 / 0.959 | |
| | Repair targets the right shapes with a permitted tool, all 750 samples | 0.948 | |
|
|
| ## License |
|
|
| Apache-2.0, following the base model. |
|
|
